1. Field of the Invention
The present invention relates to a pattern identification technique.
2. Description of the Related Art
A face identification technique of identifying the face of an individual is an example of identification techniques in pattern recognition, typically, a technique of identifying an object in image data with an object in another image. In this specification, pattern identification refers to determination of a difference between individual patterns (e.g., a difference between persons as individuals). On the other hand, pattern detection refers to determination of the category of individuals without distinguishing between them (e.g., detection of faces without distinguishing between individuals).
An example of the face identification technique is a method as disclosed in Baback Moghaddam et al., “Beyond Eigenfaces: Probabilistic Matching for Face Recognition” (M.I.T. Media Laboratory Perceptual Computing Section Technical Report No. 443), and “Probabilistic Visual Learning for Object Representation” (IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, July 1997). This is an algorithm capable of performing face registration and additional learning in real time by replacing an individual identification problem using a face with a 2-class identification problem of a feature class called a differential face.
For example, in face identification using a generally well-known support vector machine (SVM), n SVM discriminators for discriminating between a registered person's face and other faces are required to identify the faces of n persons. SVM learning is necessary to register a person's face. SVM learning requires a person's face to be registered and a large amount of face data of already registered persons and other persons, and requires a very long calculation time. Therefore, a general approach is to perform calculations beforehand. However, the method of non-patent reference 1 can make additional learning practically unnecessary by replacing the individual identification problem with the following 2-class identification problem.
Intra-personal class: a class of variation features such as an illumination variation, expression, and direction between images of the same person
Extra-personal class: a class of variation features between images of different persons
Assuming that the distributions of the two classes described above are constant regardless of any specific individual, discriminators are designed by resolving the individual face identification problem into the above-mentioned, 2-class identification problem. A large number of images are prepared, and learning is performed for a discriminator for discriminating between the class of variation features of the same person and the class of variation features between different persons. A new registrant need only hold a face image (or the result of extraction of a necessary feature). When performing discrimination, a differential feature is extracted from two images, and the discriminator determines whether the two images show the same person or different persons. This obviates the need for learning of the SVM or the like when registering the face of an individual, and makes it possible to perform the registration in real time.
In an apparatus and method of identifying a pattern (an object in an image, more specifically, a person's face) as described above, variations between a registration pattern and authentication pattern deteriorate identification performance. This is the variation of an object (person's face) to be identified. Practical examples are variations caused by the illumination condition, the direction, the posture, hiding by another object, and the expression. If the variation as described above increases, the identification performance largely deteriorates.
To solve this problem, patent reference 1 (Japanese Patent Laid-Open No. 2003-323622) ensures the robustness against variations by performing pattern matching a plurality of number of times for each partial region, removing outliers from the results, and integrating the degrees of matching of the partial regions.
Also, patent reference 2 (Japanese Patent Laid-Open No. 2006-268825) performs determination by quantizing the feature amount of each partial region, dividing the feature amounts into several subgroups, calculating the weighted sum of the quantized feature amounts in each subgroup as a new partial feature amount, and integrating the new partial feature amounts. This makes it possible to perform determination by paying attention to matching between the plurality of partial regions.
Unfortunately, room for improvement in performance presumably remains if outliers are simply removed with respect to the similarity of a plurality of partial regions and the weighted average is simply calculated. For example, if there is an error in the setting of the plurality of partial regions described above, the possibility that the error can sufficiently be corrected by the above-mentioned processing alone is probably low. In particular, if the similarity of a partial region having a heavy weight is affected by a variation, the weighted average of the similarities between partial regions increases the influence on the identification performance. Therefore, this processing is not satisfactorily robust against variations.
To maintain the identification performance even in an environment in which variations are large like those of human faces and the image sensing conditions are various, it is perhaps effective to incorporate processing which, if there is an error in the setting of the plurality of partial regions described above, can absorb the error to some extent.
In applications to digital cameras and Web cameras, the identification performance desirably does not deteriorate even when the variations in image sensing conditions and patterns (for example, size, direction, and expression) are large. It is very important to design a method that performs identification from the similarity of each partial region and does not deteriorate the identification performance even if there is an error to some extent in the setting of partial regions.